Importance of Data Science Integration For Startups

On our journey to understand the revolutionization of data science in various industries on the continent, it is realized that data science is slowly becoming the backbone of many growing businesses. As the continent with one of the highest growing populations of entrepreneurs in the world, it is also recognized that Africa is a startup frenzy today. The integration of data science however provides entrepreneurs and startups with the competitive edge needed to succeed on the market.

“The entrepreneurial potential in data sciences is immense,” states Pinkesh Shah who is a Data Scientist and CEO at the Institute of Product Leadership.

Technological advancements are critical for businesses to succeed and grow in this ever-increasing digital world. Typically big data is found to be used by large enterprises that can afford to hire data scientists to churn the information. However, thanks to the democratization of tech, some tools can be used by small and medium-sized companies to both gather big data and to use it to make good business decisions – decisions that will help them be competitive and grow.

The initial phase of a business where it is still considered a startup often has several aspects to be figured out and decided on. The application of data science usually plays a crucial role in determining the success of the startup. Data extraction is imperative for a startup to grow and molt from a fledgling to a soaring eagle. Data provides startups with the knowledge on what to improve on their products and services. The extraction and analysis of data allow startups to make a much more informed decision when it comes to a sales and marketing strategy. Knowing customer requirements beforehand makes marketing campaigns more customer-oriented.

The incorporation of Data Science into a startup allows entrepreneurs to keep up with competition and trends. To keep up with the competition and the trend, there is the need to know the activity of customers. The only way to do that is when a predictive model is created that can monitor the demand of your target market. Predictive modeling is a statistical technique using machine learning and data mining to predict and forecast likely future outcomes with the aid of historical and existing data.

A product or service is never too perfect, even the best product or service constantly needs improvements to meet the demands of consumers. Data Science makes it possible for entrepreneurs to see if their products or services can still be improved. A product experiment can be conducted through the use of data models to find out product changes or to develop a new feature. This will always keep your startup ahead of its competitors.

Let us take a moment to paint a picture, Business A and Business B intend to test the performance of their products on the market. Business A plans on doing this the traditional way which involves a lot of effort and time. Business B however intends on using a designed model to perform the same test and validation. The difference between these two businesses is that Business B can cut off almost fifty percent of the production turnaround time just by using the designed model that can test and validate the product performance. Business B goes the extra mile to use a data model to find out possible improvements on products that they currently have. The reduction of the turnaround time and the use of a data model to identify possible improvements on products gives Business B a competitive advantage over Business A.

The efficient use of data allows businesses to reduce wastages by analyzing the performance of different marketing channels and focusing on those offering the highest ROI. It is only logical for businesses to use the available data to make better, more informed business decisions, while at the same time improving the clients’ or customers’ experience.

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